Hill_Jenny_201706_PhD_thesis

Designing Green Roofs for Low Impact Development:
What Matters, and Why?
by
Jenny Charlotte Hill
A thesis submitted in conformity with the requirements
for the degree of Doctor of Philosophy
Graduate Department of Civil Engineering
University of Toronto
© Copyright by Jenny Hill 2016

ii
Designing Green Roofs for Low Impact Development:
What Matters, and Why?
Jenny Charlotte Hill
Doctor of Philosophy
Graduate Department of Civil Engineering
University of Toronto
2016
Abstract
This thesis assesses the performance of green roofs primarily as hydrologic systems and as components in
biogeochemical cycles; asking: What are they made from? Which design parameters are most influential?
What are the relative impacts? Can the findings be explained?
A multivariate experiment (n = 24) at the Green Roof Innovation Testing laboratory (GRITlab) between
May 2013 - April 2015 revealed that irrigation has the greatest effect on annual runoff coefficient,
compared to type of planting medium or planting depth (𝑥̅ = 0.5). Switching between Sedum. or native
species planting made no significant difference. NRCS curve numbers (𝑥̅ = 90) and peak runoff
coefficients (𝑥̅ = 0.1) were considered robust, unchanging with any of the design factors.
Water extractable total phosphorus in 3.5-year-old media had been unaffected by irrigation, depth or
planting compared to the overall difference between the compost or mineral basis (90 ppm and 46 ppm
respectively). Electrical conductivity was higher in water discharged from the mineral media; in situ
measurements are highly variable, complicated by the heterogeneity of the materials. Higher
concentrations of humic acids were found at in the water discharged from the compost.
The water retention curve (WRC) of ten media components and mixtures were used to explore the bi-
modal distribution of inter-particle voids and intra-particle pore spaces and to explain why the non-linear
storage capacity of the materials. The water retention capacity was inversely related to saturated

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hydraulic conductivity; both macroscale properties were highly dependent on the size of the lowest decile
fraction of particle sizes. Media components with high organic matter content were assessed for
wettability using contact angle measurements.
Past and current practices in green roof construction were considered by sampling media from thirty-three
green roofs. Most planting media were compost based with high organic matter (OM), or mineral based
with very low OM. Bulk density, particle density and porosity were all dependent on OM, as were the
hydrological properties of water retention capacity and permeability. An average 10% loss of depth was
observed across all installations regardless of their age or organic matter content.

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Acknowledgments
Firstly, a big shout out to D.Dub, Primary Sponsor of my dreams (again) and to my parents who
successfully taught me that I could be anything (whilst failing to mention I couldn’t be everything).
Sincere thanks to both of my supervisors, and excellent support team: Dr. Jennifer Drake who tolerated
my most frustrated and frustrating moments and Dr. Brent Sleep who invited me to join the school and
refrained from expelling me in early 2016. I am also indebted to Dr. Bryan Karney, for the existential
crisis incited to make my research more meaningful, and to Professor Liat Margolis, who had the
extraordinary vision and energy to create the GRITlab.
It has been a great joy reconnecting with Terry McGlade, who successfully brought in Flynn Canada as
sponsors. Every week I looked forward to the industry gossip and coffee. At Flynn I also valued highly
the practical advice of Becky Murphy and colleagues, and appreciated the interest that Mark Agius placed
in the partnership. I must also acknowledge the Natural Sciences and Engineering Research Council of
Canada (NSERC) for providing the federal scholarship that supported three years of this work.
At the GRITlab and in Civil Engineering I have taken great pleasure in sharing the sunshine with (and
benefitted from research collaboration with): Matt, Catherine, Eli, Gabrielle, Allan, Michael, Humberto,
Raquel and Scott. The GRITlab is sponsored by: DH Water Management Services Inc., GroBark, IRC
Group, Toro, and Tremco Roofing and supported by grant funding from the City of Toronto Environment
Office, Ontario Centers of Excellence, RCI Foundation, the Connaught Fund and the Landscape
Architecture Canada Foundation.
I am delighted to have received many interesting opinions and useful guidance from the professional
friends I acquired in environmental engineering and allied industries during my studies. Finally, I am
grateful to all of the owners and custodians of green roofs for facilitating physical access and in sharing
their insights.

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Table of Contents
ABSTRACT ............................................................................................................................................... II
ACKNOWLEDGMENTS........................................................................................................................IV
TABLE OF CONTENTS...........................................................................................................................V
LIST OF TABLES....................................................................................................................................IX
: INTRODUCTION ......................................................................................................1
RESEARCH OBJECTIVES ...............................................................................................................7
Context.....................................................................................................................................7
Objectives ................................................................................................................................8
BACKGROUND ............................................................................................................................10
The Green Roof as a Reservoir..............................................................................................10
The Green Roof as an Orifice................................................................................................18
The Green Roof as an Evaporation Pan................................................................................19
THESIS ORGANIZATION..............................................................................................................21
AUTHORSHIP ..............................................................................................................................22
: INFLUENCES OF FOUR EXTENSIVE GREEN ROOF DESIGN VARIABLES
ON STORMWATER HYDROLOGY.....................................................................................................23
INTRODUCTION...........................................................................................................................24
METHODS ...................................................................................................................................26
Green Roof Innovation Testing laboratory............................................................................26
Theory and Calculations........................................................................................................28
RESULTS AND DISCUSSION ........................................................................................................30
Local Climate ........................................................................................................................30
Volumetric Runoff Coefficients..............................................................................................31
Event-based Analysis.............................................................................................................36
Peak Flow..............................................................................................................................37
CONCLUSIONS ............................................................................................................................39
: INFLUENCES OF FOUR EXTENSIVE GREEN ROOF DESIGN VARIABLES
ON ANNUAL WATER BALANCE ........................................................................................................41
INTRODUCTION...........................................................................................................................42
METHODS ...................................................................................................................................44

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Green Roof Innovation Testing laboratory............................................................................44
Theory and Calculations........................................................................................................45
Irrigation and Water Retention .............................................................................................46
Winter Climate and Snow Accumulation...............................................................................48
Winter Cvol..............................................................................................................................54
Annual Cvol.............................................................................................................................56
CONCLUSIONS ............................................................................................................................57
: PHYSICOCHEMICAL PROPERTIES OF EXTENSIVE GREEN ROOF
PLANTING MEDIA .................................................................................................................................59
INTRODUCTION...........................................................................................................................60
BACKGROUND ............................................................................................................................60
Phosphorus............................................................................................................................60
Electrical Conductivity..........................................................................................................62
METHODS ...................................................................................................................................64
Green Roof Experimental Set up ...........................................................................................64
Phosphorous..........................................................................................................................64
Phosphorous..........................................................................................................................67
CONCLUSIONS ............................................................................................................................74
: THE INFLUENCE OF DEPTH AND POROSITY ON THE HYDRAULIC
PROPERTIES OF GREEN ROOF PLANTING MEDIA ....................................................................76
INTRODUCTION...........................................................................................................................77
METHODS ...................................................................................................................................80
Medium property measurements............................................................................................80
Water Retention Parameters..................................................................................................81
Density and porosity..............................................................................................................83
WRC parameters....................................................................................................................85
System water storage.............................................................................................................92
Hydrophobicity, wetting and shrink/swell characteristics.....................................................95

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CONCLUSIONS ............................................................................................................................96
: COMPARISONS OF EXTENSIVE GREEN ROOF MEDIA IN SOUTHERN
ONTARIO 98
INTRODUCTION...........................................................................................................................98
METHODS .................................................................................................................................100
System Properties ................................................................................................................101
Physical Properties..............................................................................................................102
Chemical Properties............................................................................................................103
RESULTS AND DISCUSSION ......................................................................................................104
Age of installation................................................................................................................104
Particle composition............................................................................................................105
Planting medium/Water Interactions...................................................................................107
Chemistry.............................................................................................................................116
CONCLUSIONS ..........................................................................................................................117
: CONCLUSIONS.....................................................................................................118
THE EXTENSIVE GREEN ROOF AS A RESERVOIR......................................................................118
Irrigation .............................................................................................................................118
Planting Medium .................................................................................................................120
Depth ...................................................................................................................................120
Planting type........................................................................................................................121
THE EXTENSIVE GREEN ROOF AS AN ORIFICE.........................................................................121
THE EXTENSIVE GREEN ROOF AS AN EVAPORATION PAN .......................................................122
FURTHER WORK.......................................................................................................................124
Irrigation .............................................................................................................................124
Cisterns................................................................................................................................125
Nutrition versus pollution....................................................................................................125
Development of Organic Matter..........................................................................................125
FINAL COMMENTS: THE ‘BEST’ EXTENSIVE GREEN ROOF?....................................................125
: REFERENCES .......................................................................................................128
APPENDIX A: GLOSSARY ..................................................................................................................150
APPENDIX B: DATA RELATING TO CHAPTER 2.........................................................................154
APPENDIX C: DATA RELATING TO CHAPTER 3 ........................................................................166

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APPENDIX D: DATA RELATING TO CHAPTER 4 ........................................................................169
APPENDIX E: DATA RELATING TO CHAPTER 6.........................................................................171
APPENDIX F: SUCCESS AND SUCCESSION...................................................................................174
Introduction ......................................................................................................................................175
APPENDIX G: GONE WITH THE WIND..........................................................................................186

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List of Tables
Table 1-1 Summary of green roof papers resulting in SCS curve numbers. *Getter et al (2007) were
studying effect of slope, hence the range of CN reflecting 2% to 25% slope. ............................................13
Table 2-2 Physical data for grit planting media, according to manufacturer’s ASTM 2399 report (Bioroof
Systems, 2011) ............................................................................................................................................27
Table 4-1 Levels of four experimental variables being considered at the GRITlab....................................64
Table 4-2 Subset of green roof modules tested for TP in discharged water................................................65
Table 4-3 Group mean volumetric runoff coefficients for six extensive green roof design combinations.
*n=2, encompassing both types of vegetation, apart from case E, where n = 1 (meadow planting only). .69
Table 5-1 Identity and shared sources of ten sample materials for analysis and comparison ....................80
Table 5-2 Density, porosity and organic matter content of ten porous test materials. ................................85
Table 5-3 the van Genuchten parameters from the fitted curves arising from the evaporative drying of ten
test materials................................................................................................................................................87
Table 5-4 System static and dynamic air and water properties for ten samples..........................................93
Table 5-5 Dynamic contact angle data from the analysis of the biologically derived materials E-G .........96
Table 6-1 Equations used to summarize physical characteristics of the porous media.............................103
Table 6-2 Six independent and fourteen dependant variable measured on the surveyed roofs.................104
Table 6-3 Chemistry of water extracts prepared from thirty three green roof media samples. .................116
Table 0-1 Details of study green roofs in Toronto ....................................................................................176
Table 0-2 Selected planting details study 2 Genus only identified where the species is unknown (in a
proprietary seed mixture) or where multiple species have been used. ......................................................177
Table 0-1 Combinations of erosion control measures and planting methods. Red not recommended,
yellow may present some difficulty, green represents recommended combinations. ...............................189

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List of Figures
Figure 1-1 Urbanized population, data from (United Nations, Department of Economic and Social Affairs,
2014)..............................................................................................................................................................1
Figure 1-2 Where we live: The two densest urban centres are the Greater Toronto Area in the lower centre
of image and Montreal in the top right (Simmon, 2012)...............................................................................2
Figure 1-3 The area of combined sewers within the City of Toronto, from (Di Gironimo et al., 2013).......2
Figure 1-4 The huge increase (55 % from 10 %) in runoff water resulting from storm events on a post-
development landscape with a high degree of pervious cover. Image abridged from (Federal Interagency
Stream Restoration Working Group (FISRWG), 1998).................................................................................3
Figure 1-5 The intersection and overlap between Low Impact Development (left) and Green Infrastructure
(right).............................................................................................................................................................5
Figure 1-6 The rise in 'green roof' articles from Figure 1 in a hydrological review paper by Li and
Babcock (2014) .............................................................................................................................................7
Figure 1-7 Summary of previous studies assessing volumetric retention: A (Teemusk and Mander, 2007);
B (Cronk, 2012); C (Hilten et al., 2008; Prowell, 2006); D (Schroll et al., 2011); E (Berghage et al.,
2010); F (Gregoire and Clausen, 2011); G (VanWoert et al., 2005); H (Ma et al., 2012); I (Moran et al.,
2004); J (Van Seters et al., 2009); K (Hathaway et al., 2008); L (Voyde et al., 2010); M (Uhl and Schiedt,
2008); N (Starry, 2013); O (Carter and Rasmussen, 2006); P (Burszta-Adamiak, 2012); Q (Getter et al.,
2007); R (Palla et al., 2012).........................................................................................................................10
Figure 1-8 Toronto rainstorm depth distribution, from 1937 – 1983 Bloor Strreet rain gauge data. ..........11
Figure 1-9 Conceptual relationship between maximum theoretical storage capacity (mm) and annual Cvol
.....................................................................................................................................................................12
Figure 1-10 Derived from (e.V., 2008), with the caption “All figures relate to locations with annual
precipitation values of 650 – 800 mm where monitoring has been performed over a period of several
years.”..........................................................................................................................................................16
Figure 1-11 Conceptual illustration of effects on hydrograph ....................................................................18
Figure 1-12 Conceptual closed system model combining a green roof with a cistern ................................19

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Figure 1-13 One part of the irrigation system on the Rottman School of Management, University of
Toronto. This system uses recycled and/or harvested rainwater to irrigate extensive green roofs. ............20
Figure 2-1 Typical layering of a built-up extensive green roof system.......................................................24
Figure 2-2 Schematic of GRITlab, illustrating the randomized layout of the four experimental variables.
Key - colours in each rectangular module can be read from west to east. Vegetation: dark = Meadow,
light = Sedum. Planting medium: dark = biological, light = mineral. Irrigation: dark = daily, light =
sensor, mid = none. Construction depth: dark = 15 cm, light = 10 cm. ......................................................26
Figure 2-3 Local weather at GRITlab, Toronto between May-October 2013 and May-October 2014, the
duration of the green roof study. .................................................................................................................30
Figure 2-4 Annual exponential cumulative distribution of storm depths in Toronto, ON. According to
2013 and 2014 GRITlab data and historical records (1937-1983) from Bloor St.......................................31
Figure 2-5 Regression tree for the runoff coefficients (Cvol) determined on twenty-three extensive green
roofs over 12 summer months encompassing May-October 2013, and May-October 2014 ......................32
Figure 2-6 Monthly group mean volumetric runoff coefficients for 23 green roof modules during the
periods between May-October 2013 and May-October 2014. ....................................................................33
Figure 2-7 Monthly group mean volumetric runoff coefficients for four design factors during the during
the periods between May-October 2013 and May-October 2014. ..............................................................34
Figure 2-8 Box plot of volumetric runoff coefficients over rainstorm events in 2013 and 2014, grouped
according to medium type and antecedent volumetric water content over the range 0 – 0.55 v/v. Group
means indicated with ‘X’ and connected within the medium type..............................................................36
Figure 2-9 Regression tree for the NRCS Curve Numbers determined on twenty three extensive green
roofs over 12 summer months encompassing May-October 2013, and May-October 2014.......................37
Figure 2-10 Validation of peak based runoff coefficients using Rational method to calculate peak flow
(Qp) and compared to experimental data for twelve, 2015 rainstorm events. Error bars represent the
standard deviation of all twenty-four module’s peak flows per event.........................................................38
Figure 3-1 GRITlab modules raised above the roof deck to accommodated monitoring equipment..........44

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Figure 3-2 Extensive green roof annual total water retention for months October 2013 –September 2014,
grouped by irrigation program.....................................................................................................................47
Figure 3-3 Monthly water retained group means for three levels of irrigation between October 2013 to
September 2014. Reference ET from the GRITlab weather station............................................................47
Figure 3-4 Input and output volumes associated with two irrigation programs ..........................................48
Figure 3-5 Winter months climate normal snow cover, daily minimum temperatures and daily
precipitation depth from 1981-2010 data in Toronto, Ontario (Environment Canada, 2013).....................49
Figure 3-6 Mean daily air temperature (dashed line) from GRITab and precipitation record (bars) from
Toronto City weather station for the periods encompassing November 2013 to April 2014, and November
2014 to April 2015. .....................................................................................................................................50
Figure 3-7 Twenty-four modules accumulated snow depth throughout winters 2013-14 and 2014-15,
plotted over ground level data.....................................................................................................................51
Figure 3-8 Moran’s I from winter 2013-14 centred about zero, indicate no significant geospatial clustering
or trends in the snow depth across the GRIT lab experiment......................................................................52
Figure 3-9 Mean snow depth, grouped by medium type (top), and irrigation (bottom) throughout winters
2013 and 2014. ............................................................................................................................................53
Figure 3-10 Native meadow vegetation mix grown on: a) biological medium with daily irrigation, b)
mineral medium with daily irrigation, and c) mineral medium without irrigation. Photographs taken 20
September 2013 (University of Toronto, 2013). .........................................................................................54
Figure 3-11 Mean volumetric runoff coefficients from 23 modules, over 12 months of summertime events
May-Oct in 2013 and 2014 and 12 months of wintertime balance, Nov-April in 2013-2014 and 2014-
2015.............................................................................................................................................................54
Figure 3-12 Group mean runoff coefficients per month through May 2013 to April 2015. .......................55
Figure 3-13 Group mean runoff coefficients by irrigation program, for months through November – April
2013-14 and 2014-15...................................................................................................................................56

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Figure 3-14 Annual volumetric runoff coefficients for extensive green roofs, calculated from 24 months
of data spanning May 2013- April 2015. Each cell contains: Design factor ‘level’, group mean value, and
(# modules)..................................................................................................................................................57
Figure 4-1 Summary of previous studies assessing the total phosphorous discharge from extensive green
roofs: A (Gregoire and Clausen, 2011); B (Toland, 2010); C (Berndtsson et al., 2006); D (Teemusk and
Mander, 2007); E (Van Seters et al., 2009); F (Harper et al., 2015); G (Beck et al., 2011). Many of the
mixtures contain lightweight expanded aggregate (LEA)...........................................................................61
Figure 4-2 Summary of previous studies which state the electrical conductivity of discharge from
extensive green roofs: A (Beecham and Razzaghmanesh, 2015); B (Gnecco et al., 2013); C (Göbel et al.,
2007); D (Buffam et al., 2016); E (Van Seters et al., 2009); F (Buccola and Spolek, 2011).......................63
Figure 4-3 The water extractable total phosphorous in twenty-four, 3.4-year-old green roof modules is
distinguished only by the type of planting medium. ...................................................................................68
Figure 4-4 TP in discharge water from six green roof modules.................................................................68
Figure 4-5 Regression tree illustrating the relative influence of three design factors on the TP
concentrations in samples taken March/April 2016. ...................................................................................69
Figure 4-6 The influence of green roof medium type on the physicochemical parameters, pH and
electrical conductivity. ................................................................................................................................71
Figure 4-7 Calibration of 5TE sensor in biological planting medium (top), and mineral based medium
(bottom).......................................................................................................................................................72
Figure 4-8 Range of ε0 in eleven green roof modules containing bioloigcally derived planting medium
(left), and mineral based green roof planting medium (right). ....................................................................73
Figure 4-9 Irrigation makes a more significant impact on pore water electrical conductivity in April 2016,
than any other design factor: planting medium type, depth or planting type. .............................................74
Figure 5-1 Green roof matric pressure as a function of medium depth under static equilibrium with
maximum water storage. Where θ = volumetric water content, and θs = saturated volumetric water content
.....................................................................................................................................................................78
Figure 5-2 Drying curve data from the analysis of ten samples. Grey circles are raw data, lines are the
fitted curves: Bulk materials A, C, E, and G are grouped as having significant (w1 > 0.9) weighting on the

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inter-particle voids (top); bulk materials B, D, and F are grouped as having distinctly separate and more
evenly weighted van Genuchten parameters (middle); blended materials H, I, and J (bottom)..................86
Figure 5-3 The largely unimodal pore size distributions (line) plotted over the particle size distributions
(bars) found in: A: Sand, C: Poorly-graded LEA, E: ¼” Screened composted wood, and G: Shredded
Pine..............................................................................................................................................................88
Figure 5-4 The largely bimodal pore size distributions (line) plotted over the particle size distributions
(bars) found in: B: Well-graded LEA, D: Crushed brick, and F: Bark fines...............................................89
Figure 5-5 Surface detail visible under 100x magnification: left) B: LEA, centre) D: Brick particle, right)
F: Bark fragment .........................................................................................................................................90
Figure 5-6 The pore size distributions (line) plotted over the particle size distributions (bars) found in
commercial green roof planting media blends: H: Compost based - Manufacturer A, I: Mineral based -
Manufacturer A, and, J: Mineral based - Manufacturer B...........................................................................91
Figure 5-7 Modelled water storage in three 5 cm increments of green roof profile depth, for seven bulk
materials (A-G) and three commercial blended materials (H-J). ................................................................92
Figure 5-8 Regresison trees for prediction of container capacity (θ(h15)) and wilting point (θ(h15296), from
predictors ρd, ϕ, and OM. ............................................................................................................................94
Figure 5-9 Binary image from x-ray of material I particle (left), results of surface fractal analysis to show
the network of connected pores (right)........................................................................................................95
Figure 6-1: Age of thirty-three green roofs at the time of surveying and sampling..................................100
Figure 6-2 Schematic (not to scale) and photograph of the infiltrometer used for in situ measurements.102
Figure 6-3 Organic matter content of plating media recovered from thirty-three extensive green roofs;
roofs are alphabetical from oldest to most recently constructed, the dashed line crosses at 8 %..............106
Figure 6-4 Multifactor box plot of bulk and solid particle densities, divided between low (< 30%) and
high (≥ 30%) OM content..........................................................................................................................107
Figure 6-5 Relationship between maximum water content and organic matter content of green roof media
...................................................................................................................................................................108

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Figure 6-6 Multifactor box plot of particle size distribution coefficients, divided between low (< 30%)
and high (≥ 30%) organic matter content..................................................................................................110
Figure 6-7 Particle size distirbution curves from green roof planting media recovered from green roofs
with CU ≈ 16. Dashed line media CC = 0.5; solid line media CC = 5.5......................................................111
Figure 6-8 Regression tree of MWC demonstrating the relative importance of OM and interaction with
particle size parameters CU and CC............................................................................................................112
Figure 6-9 Relationship between free air space and organic matter content in green roof planting media
...................................................................................................................................................................113
Figure 6-10 Relationship between infiltration and permeability rates in eighteen green roof media samples
...................................................................................................................................................................115
Figure 7-1 GRITlab module 6E, 23 June 2015 .........................................................................................126
Figure 7-2 Toronto Botanical Garden Extensive Green Roof, 28 May 2014............................................127
Figure 0-1 Stakeholder rankings of the importance of green roof functions (n=7)...................................179
Figure 0-2 Earth Rangers Southern roof: 2005, after 2 years establishment (left), and 2013 (right)........180
Figure 0-3 George Vari Engineering Building roof, 2013 (left), and 2014 (right)....................................181
Figure 0-4 Toronto Botanical Garden sloped section, 2006 (left) and 2014 (right)..................................181
Figure 0-5 Arts and Administration green roof, University of Toronto, 2005 (left) and 2013 (right). .....182
Figure 0-6 Royal Ontario Museum scorched section detail, 2013 (left) and 2014 (right) ........................183
Figure 0-7 Depth of planting substrate on eight green roofs.....................................................................184
Figure 0-1. Preparation of pre-grown Sedum ‘mats’ (left), root penetration after two years growth on a
green roof (right). ......................................................................................................................................188
Figure 0-2. Anchors to retain pre grown mats in high wind velocity situations. ......................................188
Figure 0-3 Tenting of polymer mesh over native wildflower seed mixture on an extensive green roof...190

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Figure 0-4. Evaporation of water from; a) psyllium husk amended compost, b) Polyacrylamide amended
compost. ....................................................................................................................................................192
Figure 0-5 Change in percolation rate after replicated measurements in a) psyllium husk, and b)
polyacrylamide amended compost. ...........................................................................................................193
Figure 0-6. Water retention as a proportion of the material dry weight in psyllium husk (PH) amended
compost and polyacrylamide (PAM) amended compost...........................................................................194

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List of Appendices
Appendix A: Glossary 150
Appendix B: Data relating to Chapter 2 154
Appendix C: Data relating to Chapter 3 166
Appendix D: Data relating to Chapter 4 169
Appendix E: Data relating to Chapter 6 171
Appendix F: Success and Succession 174
Appendix G: Gone with the Wind 186

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Symbols and Abbreviations
A Area (m2
)
Abs400 Absorbance at 400 nm
Agr Area of green roofs (m2
)
b Molality (mol/kg)
CC Coefficient of curvature
CN Curve number
Cpeak Peak runoff coefficient
CU Coefficient of uniformity
Cvol Volumetric runoff coefficient
DOM Dissolved organic matter
dx Particle size (mm) such that x % of the mixture comprises particles finer than dx
ET Evapotranspiration
F F-distribution parameter
FAS Free air space
FEEM Fluorescence Excitation-Emission Matrix
FLL Forschungsgesellschaft Landschaftsentwicklung Landschaftsbau
g Acceleration due to gravity (m/s2
)
h Capillary pressure head (cm)
H Hydraulic head (m or cm)
Ht Hydraulic head at time t (cm)
i Rainfall intensity (mm/min)
i.d. Internal diameter
Ia Initial abstraction (mm)
Irr. Irrigation
Kf Permeability (field conductivity) (mm/s)
Ksat Saturated hydraulic conductivity (m/hr)
L Depth of the medium sample in the column (cm)
LEA Lightweight expanded aggregate
LID Low Impact Development
MWC Maximum water capacity (%)
NTU Nephelometric Turbidity Unit
OM Organic matter (%)
n Sample size

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ni Unitless pore size distribution parameter
nm Nanometers
NRCS Natural Resources Conservation Service (U.S.)
p p value: the smallest level of significance for which the null hypothesis is rejected
pann. Annual precipitation (mm)
P Precipitation depth (mm)
PAW Plant available water
PCC Pearson product-moment correlation coefficient
pF Negative log10 of head in cm
pH Negative log10 of [H+
]
PSD Particle size distribution
PWP Permanent wilting point (= 1.5 MPa)
Q Discharge (mm)
Qp Peak flow rate (mm/min)
r Pore radius (μm)
R Universal gas constant
RH Relative humidity (%)
S Theoretical storage (mm)
WRC Water retention curve
t Time (s)
T Temperature (°C)
TP Total phosphorous (mg/L)
V Volume (L)
WETP Water extractable total phosphorous (mg/kg)
wi Weighting factor
𝒙̅ Arithmetic mean of the sample
Z Statistical Z-score
z Elevation head (cm)
αi Fitting parameter (cm-1
)
γ Interfacial tension (N/m2
)
δ Receding contact angle (°)
ɛ0 Theoretical dielectric permittivity of dry media
ɛb Bulk dielectric permittivity
ɛp Pore water dielectric permittivity

xx
ζ Parameter used for fitting exponential annual rainfall depth distribution
θ Volumetric water content (v/v)
θr Irreducible water content (v/v)
θant. Antecedent volumetric water content (v/v)
θs Saturated water content (v/v)
λ Ratio between S and Ia in NRCS curve number calculations
ρw Density of water (kg/m3
)
μS/cm MicroSiemens per centimeter
ρm Maximum (wet, saturated) medium density (g/cm3
)
ρd Dried bulk medium density (g/cm3
)
ρs Mean solid particle density (g/cm3
)
σ Standard deviation
σb Bulk electrical conductivity (μS/cm)
σp Pore water electrical conductivity (μS/cm)
σw Discharge water electrical conductivity (μS/cm)
Σ Sum
ϕ Porosity
ψm Pressure potential, syn. matric potential (kPa)
ψo Osmotic potential (kPa)
ψt Total water potential (kPa)
ψz Gravitational potential (kPa)

1
: Introduction
In 2014, the United Nations announced that, since 2007, over half of the world’s population were living
in urbanized environment (Figure 1-1). Owing to geographical and climatic factors the Canadian
population are well ahead of this trend, with over 80 % of us living in urban areas, as of 2014 when the
data was last collated (United Nations, Department of Economic and Social Affairs, 2014).
Figure 1-1 Urbanized population, data from (United Nations, Department of Economic and Social Affairs,
2014)
The City of Toronto (Figure 1-2) is the fourth most populous in North America (Contributors, 2016) and
the largest within the Great Lakes Basin, a watershed governed under a Joint Commission with the United
States of America (International Joint Commission, 2016). Whilst the topography of Toronto’s inner city
core determines that the landscape drains almost directly into Lake Ontario to the south (Toronto Region
Conservation Authority, 2016), urban development has driven stormwater to be rerouted into drains to
reduce flooding in the streets. Like many other urban areas established over a century ago, Toronto has
aging and somewhat undersized wastewater/stormwater infrastructure for the population now depending
upon it.
0
25
50
75
100
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
PopulationUrbanized(%)
WORLD
Canada

2
Figure 1-2 Where we live: The two densest urban centres are the Greater Toronto Area in the lower centre of
image and Montreal in the top right (Simmon, 2012)
The combined wastewater/stormwater sewer system outlined in Figure 1-3, serves the oldest and densest
parts of the city, and is prone to overflowing contaminated water directly into natural watercourses during
heavy rainstorm events (City of Toronto, 2016a). Approximately 25% of the city is served by the
combined sewer, which has 80 outfalls where direct overflows can occur (Podolsky, 2013). Annual
statistics regarding the number of overflow events is not available to the public, but 42 events were
reported between April – October 1991 (City of Toronto, 2010; Podolsky, 2013).
Figure 1-3 The area of combined sewers within the City of Toronto, from (Di Gironimo et al., 2013).

3
The demands upon the combined sewer system are increasing for two reasons. Firstly, through urban
densification, a greater population are producing wastewater within the area served. The population of the
Council Area ‘Toronto and East York’ (encompassing most of the combined sewer area) rose over 8%
between 2001 and 2011, to 7.32 thousand people/km2
(City of Toronto, 2016b). The base flow arising
from people’s activities fluctuates with their daily and seasonal activities. Overlaid onto this is the sudden
additional flow during and after rainstorm events. In Southern Ontario the intensity of rainstorm events is
expected to increase under current climate change predictions (Bates, 2008; SENES Consultants Ltd.,
2011). As most urban areas comprise a high proportion of impervious surfaces compared to undeveloped
landscapes, this stormwater runoff flows hot, fast and dirty (Figure 1-4).
Figure 1-4 The huge increase (55 % from 10 %) in runoff water resulting from storm events on a post-
development landscape with a high degree of pervious cover. Image abridged from (Federal Interagency
Stream Restoration Working Group (FISRWG), 1998)
As combined sewers contain a mixture of stormwater, blackwater (sewage from toilets) and greywater
(e.g. washing water), the entire wastewater stream must be treated as hazardous and usually receives
costly multi-stage treatment at municipal plants before discharge into natural systems. For this reason,
reducing the volume of all three sources is desirable from economic and environmental perspectives.
In Toronto’s contemporary stormwater management policies, there are three primary, overarching
principles which development plans must achieve (City of Toronto, 2006). These may be summarized as:
Water Balance:
1. That ≤ 50% of annual precipitation becomes runoff water. i.e. ≥ 50% annual precipitation
must be retained on site,
2. That a 24 hour, 5 mm rainfall event must be entirely (100%) retained on site.

4
Water Quality:
1. That ≥ 80% of total suspended solids are removed from runoff water leaving the site,
2. For lakefront discharges there are also variable, seasonal E. coli limits.
Water Quantity:
1. Variable guidelines exist for the maximum permitted flow limits from sites according to
their size and location. For sites < 2 ha, the Rational method is permitted to make the
necessary calculations.
2. Erosion control criteria focus on larger development sites, particularly adjacent to
sensitive areas, such as ravines.
3. Peak discharge flow to municipal infrastructure during a 2 year return period storm event
must not exceed the lower of:
a. the capacity of the downstream municipal system, or
b. the flow resulting from a runoff coefficient of the ‘pre-developed conditions’,
capped at 0.5.
Whilst these targets may be achieved by the construction of grey, concrete infrastructure including larger
pipes and ponds, or vaults in dense urban settings; there are infrastructural benefits from adopting a
decentralized approach to managing city stormwater. Low Impact Development (LID) is both a
conceptual approach promoting source control, and a selection of tools used to reduce stormwater flow
and protect developed watersheds in urbanized landscapes. By minimizing the imperviousness of a site
and treating rainwater as a commodity rather than a nuisance, the burden on trunk infrastructure can be
reduced and the hydrologic behaviour of the watershed could more closely mimic that of a natural
ecosystem. Some LID techniques are based on the use of transpiration by vegetation, resulting in some
aesthetic or amenity benefit from the planting, and so creates an overlap with the term ‘Green
Infrastructure’ (GI) (see Figure 1-5). Green Infrastructure Ontario provide the following broad definition:
“…natural vegetative systems and green technologies that collectively provide society with a multitude of
environmental, social and economic benefits.”(Cirillo and Podolsky, 2012)
The US EPA focus their definition of green infrastructure on the nexus with LID:
“…systems and practices that use or mimic natural processes to infiltrate, evapotranspirate (the return of
water to the atmosphere either through evaporation or by plants), or reuse stormwater or runoff on the
site where it is generated.”(United States Environmental Protection Agency, 2014)

5
Figure 1-5 The intersection and overlap between Low Impact Development (left) and Green Infrastructure
(right)
Subsurface infiltration is often a primary objective of LID; wherever possible projects are installed on the
land surface and include some sort of subgrade enhanced infiltration component. Examples include
permeable paving and vegetated bio-retention cells. Where there is a constraint on the available land, or
infiltration is otherwise hindered, cisterns are an alternative to retain stormwater for reuse, or later
discharge. Another option for sites that have limited space to provide for subsurface infiltration is to
design for retention of stormwater on roof tops. Where this is simply conducted using a weir/overflow
system, this is termed a blue roof and is relatively uncommon in Ontario (Cheung, 2016; Crawford, 2013;
Duncan, 2015). A more popular option is a green roof, in which vegetation in a supporting planting media
are assembled to emulate a naturalized setting on a building rooftop. As green roofs create the potential
for habitat, reduce urban heat island effect, provide amenity value and insulate their supporting building
(Castleton et al., 2010), they are often included as a key element of urban green infrastructure; they share
some of these characteristics with parks and urban forest.
A number of countries in Western Europe have a long standing tradition of using cut sod and other basic
materials to construct green roofs (Almssad and Almusaed, 2015; van Hoof and van Dijken, 2008).
However, in North America, the interest in building vegetation is more recent, popularized by the
Urban forest
Green walls
Parks
Green roofs
Swales
Bioretention cells
Wetlands/
Ponds
Rainwater
harvesting
Perforated pipes/
Soakaways
Permeable paving
Low Impact Development Green Infrastructure

6
environmental movement of the late 20th
Century (Mentens et al., 2006). The later onset of construction is
evident in the carefully chosen and specifically engineered products found in most local green roofs. The
development of green roof systems in the North American market is still a rapidly advancing field for
several reasons:
- In the 1980s, the German Forschungsgesellschaft Landschaftsentwicklung Landschaftsbau e.V.
(FLL) prepared the first comprehensive document detailing many aspects of green roof planning,
maintenance and monitoring. Since then, the easiest course was to follow their recommendations
(e.V., 2008; Philippi, 2005).
- The industry is still dominated by aesthetic concerns, with many stakeholders viewing the
infrastructural benefits as convenient bonuses. There is rarely a clear intention what the ‘primary’
function of most installations should be, so that designs are not optimized to meet specific
objectives. This factor is exacerbated by the current green roof bylaw in Toronto which mandates
the construction of green roofs without specifying performance objectives other than vegetation
survival (Toronto, 2009).
- Within the field of stormwater management, there is no agreed method on how (or why) to model
extensive green roofs as part of a site-wide stormwater management strategy.
Consultants in both municipal engineering and in landscape architecture, and policy makers with the City
of Toronto, have to date, relied quite heavily upon information given to them from product manufactures,
who obviously have vested interests. In considering the stormwater management functions of an extensive
green roof, they may be conceptualized as one or more of the following traditional hydraulic structures:
- in retaining excess stormwater, they perform the function of a reservoir,
- in reducing or restricting peak flow rates, they emulate an orifice,
- or, they may be viewed as a conduit through which to empty a cistern or vault through
evapotranspiration, emulating an evaporation pan.

7
Research Objectives
Context
This thesis does not make the case for or against the construction of green roofs, for that argument has
already been successfully made, albeit sometimes grudgingly (Lstiburek, 2011). On a global scale, the
recent (2004 to date) general public interest in green roofs was at its maximum around ten years ago, with
peak Google searches in April 2006, 2007 and 2008 (Google Trends, 2016). Interest in all other Countries
and Cities are scaled against Canada and Toronto respectively, as the global centre of searches for the
term ‘green roof’. Toronto not only has the bylaw mandating construction under some development
circumstances (Toronto, 2009), but is also home to one of the most active industry advocacy
organisations, Green Roofs for Healthy Cities (2016). In the academic literature, interest levels continue
to rise (Figure 1-6)(Li and Babcock, 2014). The search term ‘green roof’ returns over 2,000 academic
journal articles published within the University of Toronto holdings so far this year (8th
August)
(Univeristy of Toronto Libraries, 2016).
Figure 1-6 The rise in 'green roof' articles from Figure 1 in a hydrological review paper by Li and Babcock
(2014)
Within this context, this thesis is based on the presupposition that extensive green roofs will continue to
be constructed for the foreseeable future, and focuses on how their design might be optimized for
stormwater control. Extensive green roofs are those constructed with the non-biotic components up to 15

8
cm in total depth, as these are the lightest systems, most commonly employed for stormwater
management and most suitable for retrofit installations (Czemiel Berndtsson, 2010). Green roofs > 15 cm
are typically constructed with amenity benefits as a driving factor, and the opportunities for their
installation are limited owing to the load they present. There is no upper limit to the depth and weight of
this type deeper ‘intensive’ type of green roof, so that in a dense urban environment so that many city
parks may double as underground parking or conceal other subterranean infrastructure. For additional
definitions associated with rooftop vegetation, see the Glossary at the end of this document.
Objectives
There are three primary research objectives of this thesis:
1. To produce responsive research regarding construction practices of extensive green roofs
Connecting with industry and considering current construction practices and beliefs and maintaining an
awareness of the multidisciplinary teams involved in the design and implementation of green roofs are
essential to producing influential findings. This work aims to produce simple parameters and interpretive
figures as decision making tools to help connect different disciplines, and academia with industry and
policy. Supporting predictions of the performance of design and maintenance configurations will lead to
recommendations to optimize green roofs according to specific storm water management objectives,
context and overarching functional priorities. In so doing, to increase the uptake and development of
useful extensive green roofs as part of our urban infrastructure.
 Connection with industry is facilitated by the NSERC IPS funding mechanism supporting three of
the four years, but is expanded by regular social and professional engagement with designers,
manufacturers, installers, maintenance crews, owners, custodians and policy makers.
 Awareness of the current practices will be gained by visiting, inspecting and sampling from as
wide spectrum of extensive green installations as is possible within the duration.
2. To characterize extensive green roof stormwater management performance in the context of the
local climate, encompassing both rainstorms and snowfall events
By determining robust coefficients for the modelling of extensive green roofs as one of three conceptual
hydraulic structures, they may be easily incorporated into site stormwater models:
As a reservoir, the important parameters are:
 the capacity of the system to retain stormwater and hold that as a plant available source to
maintain the health of the vegetation, in effect water balance. On a monthly, seasonal, and annual
basis this will be defined as a volumetric runoff coefficient (Cvol). This is calculated as the

9
fraction of water from summed discharge volume in relation to precipitation. These are often
translated into a % retention value by simply finding the ‘missing’ fraction by subtraction:
% 𝑟𝑒𝑡𝑒𝑛𝑡𝑖𝑜𝑛 = 100 × (1 − 𝐶 𝑣𝑜𝑙)
Equation 1-1
 the capacity of the system to retain stormwater on a per event basis, and in so doing contribute to
the control of stormwater volumes and helping to reduce urban flooding. For this NRCS (Natural
Resources Conservation Service) curve numbers (CN) will be determined for the extensive green
roofs based on individual event discharge volumes (Q, mm) as a fraction of the precipitation
volumes (P, mm). Details follow in Chapter 2.
As an orifice, the important parameter is:
 the reduction in peak flow provided by the system. This will be determined as a peak runoff
coefficient (Cpeak), by rearrangement and regression of the Rational method equation, in which
peak flow (Qp) is determined from peak rainfall intensity (i) and catchment area (A).
𝑄 𝑝 = 𝐶 𝑝𝑒𝑎𝑘 𝑖𝐴
Equation 1-2
As an evaporation pan, the important parameters are:
 the potential for evapotranspiration from the green roof system as a means of discharging excess
water, and
 changes in the chemistry of the system associated with the evaporation of water from media and
plants.
3. To determine the relative influence of design and irrigation practices on the stormwater
management performance of extensive green roofs
By undertaking a multi-factorial study on green roofs with mixed design combinations, the factors
influencing each of the coefficients and performance indicators in section 1.1.1 above can be identified,
ranked and quantified. The design factors shall include the choice of vegetation between Sedum or an
alternative planting palette; the choice of planting medium type, whether a granular mineral mixture, or a
biologically derived, compost based product; the depth of planting material up to 15 cm; and the
provision of irrigation under different programming.

10
Background
The Green Roof as a Reservoir
Prior to the beginning of the 21st
century, the vast majority of green roof construction and research was
undertaken in central Europe, resulting in little English language literature (Mentens et al., 2006). The
first phase of widespread green roof research began with comparisons made between green roof
installations and similar areas of ‘traditional roof’. Most studies, such as those shown in Figure 1-7, report
an aggregated precipitation retention, as this is easily measured and quite apparently a key strength of the
green roof in stormwater management. Per event, the amount retained depends largely upon the capacity
within the medium, which in turn depends on its material properties, depth, and existing moisture content.
This existing moisture in the system is determined by the climatic properties of depth of recent rainfall,
dry period since and the rate of evapotranspiration throughout that time. The publications from this type
of study vary widely in their conclusions, based on the variety of materials used and climates, in which
the test sites were located, see Figure 1-7.
Figure 1-7 Summary of previous studies assessing volumetric retention: A (Teemusk and Mander, 2007); B
(Cronk, 2012); C (Hilten et al., 2008; Prowell, 2006); D (Schroll et al., 2011); E (Berghage et al., 2010); F
(Gregoire and Clausen, 2011); G (VanWoert et al., 2005); H (Ma et al., 2012); I (Moran et al., 2004); J (Van
Seters et al., 2009); K (Hathaway et al., 2008); L (Voyde et al., 2010); M (Uhl and Schiedt, 2008); N (Starry,
2013); O (Carter and Rasmussen, 2006); P (Burszta-Adamiak, 2012); Q (Getter et al., 2007); R (Palla et al.,
2012).
32 events
62 events
153 events
31 events
84 events
48 events
91 events
15 months
154 events
9 months
3 events
83 events
97 events
72 events
18 events
70 events
63 events
3 events
0 10 20 30 40 50 60 70 80 90 100
R
Q
P
O
N
M
L
K
J
I
H
G
F
E
D
C
B
A
Average % storm water retention reported through entire study

11
The studies summarized were of varying durations, which accounts for the very low (study A – 3 events
(Teemusk and Mander, 2007)), and very high numbers (study R – 32 events (Palla et al., 2012). As
extensive green roofs have a finite capacity to store excess stormwater, there will usually be a proportion
of rainstorm events for which the capacity is insufficient. Whilst the rainstorm depth distributions vary
according to climate (Stovin et al., 2015), this remains a constraint to the overall potential retention for
any extensive system. The distribution of rainfall depths in Toronto is presented in Figure 1-8. The
coefficient for the exponential distribution (-0.119) of rainstorm depths in Figure 1-8 come from tables in
Adams and Papa (2000). A very similar distribution is in use within the City of Toronto Wet Weather
Flow Masterplan Guidelines, although the coefficient is not published (City of Toronto, 2006).
Figure 1-8 Toronto rainstorm depth distribution, from 1937 – 1983 Bloor Strreet rain gauge data.
In a conceptual reservoir, the inverse of this distribution can be used to understand the relationship
between storage (S, mm), and the proportion of annual rainfall which emerges as discharge, the
volumetric coefficient, Cvol:
𝐶 𝑣𝑜𝑙 = 𝑒−0.119𝑆
Equation 1-3
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 5 10 15 20 25 30
%TotalAverageAnnualOccurences
Storm depth (mm)
Bloor St (1937-1983) 12 hr
Bloor St (1937-1983) 12 hr

12
Figure 1-9 illustrates this relationship and how the distribution of rainstorm depths creates difficulty in
reducing annual volumetric runoff coefficient of a limited storage system such as an extensive green roof.
Adding 4 mm storage to a system with Cvol of 0.5 will reduce Cvol to 0.3, but adding another 4 mm storage
only reduces Cvol to 0.2.
Figure 1-9 Conceptual relationship between maximum theoretical storage capacity (mm) and annual Cvol
Translating the cumulative distribution of storm depths (Figure 1-8) to the ability of a system to capture
and retain such events (Figure 1-9) requires a major assumption; that the system storage capacity is
entirely available as each storm occurs. The storage capacity of an extensive green roof depends upon
how wet the planting medium is at the storm onset. This is determined by the depth of the preceding
event, the time elapsed since that event and the rate of evapotranspiration in the intervening period.
In Ontario, three studies have examined the stormwater properties of green roofs in the last eleven years.
Liu and Minor (2005) reported a net retention of 57% (Cvol = 0.43) on two green roofs on Eastview
Community Centre. Linden and Stone (2009), reported net retention of 44% (Cvol = 0.56) by a green roof
in Waterloo, and Van Seters et al. (2009) reported the annual retention of water on the roof of the
Computer Sciences building at York University as 63% (Cvol = 0.37) after 154 precipitation events (> 0
°C periods) from May 2003 – August 2005. The net retention in each of the three studies is low compared
to the overall average shown in Figure 1-7 (61 %); notably, none of these local studies include water
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Volumetricrunoffcoefficient,Cvol
Precipitation depth captured and stored (mm)
Bloor St (1937-1983) 12 hr

13
balance calculations during the winter season, preventing the establishment of a clear net annual retention
value (Dhalla and Zimmer, 2010).
A widely used parameter, for continuous simulation modelling, is the NRCS curve number (CN). As the
empirical calibration methods and calculations were developed in the USA, a number of researchers in the
United States have used this metric to describe the hydrology of their green roofs. Two recent studies in
Michigan and Chicago have been conducted under climatic conditions similar to Southern Ontario are
presented in Table 1-1.
Table 1-1 Summary of green roof papers resulting in NRCS curve numbers. *Getter et al (2007) were studying
effect of slope, hence the range of CN reflecting 2% to 25% slope.
Curve Number(s) Location Reference
86 Athens, Georgia, USA (Carter and Rasmussen, 2006)
84-90 East Lansing, Michigan, USA (Getter et al., 2007)
80 Chicago, Illinois, USA (Berghage et al., 2010)
For context, CNs for other land uses range between 98 for paved surfaces that have no storage or
infiltration capacity, to below 30 in forests on sandy soils (Mishra, 2003). CNs have local application as
they are used in single event or continuous simulations (Greenland International Consulting Inc., 2002),
which are recommended during development proposals in many Ontario conservation authorities (Central
Lake Ontario Conservation, 2010; Lake Simcoe Region Conservation Authority, 2013; Toronto Region
Conservation Authority, 2014).
The use of curve numbers for green roofs has been critiqued as: not being precise enough for runoff
simulation (Roehr and Kong, 2010), resulting in surprisingly high values and not representing the
physical differences from a natural hydrologic system (Elizabeth Fassman-Beck et al., 2015), and not
representing the variety of designs commonly employed (Roehr, 2010). However, this empirical model
provides a suitable mechanism to compact a large number of data pairs into a single metric per green roof
which can then be employed for statistical comparisons and assessment of retention performance.
1.2.1.1Design Factors
As it became more widely accepted that green roofs retain more stormwater compared to a traditional
non-vegetated roof, the next generation of research developed, comparing green roofs against one
another. In an early example, a hydrological/thermal analysis of extensive green roofs at the University of
Texas, Simmons et al. (2008) concluded:
“Green roofs are not created equal”

14
However, this particular study of six different green roof systems used complete proprietary assemblies
preventing elucidation of the critical design factors. Other studies have focused on assessing the impact of
just one or two design parameters at a time, usually through a combination of fieldwork and laboratory
experiments (Czemiel Berndtsson, 2010; Li and Babcock, 2014). Commonly considered design factors
include: the type and depth of the planting medium, the species included in the planting and the provision
of irrigation.
Planting medium
Green roof planting medium is typically comprised of an engineered mixture of graded materials
including some composted organic matter and some granular mineral components. Inspired by the
German FLL guidelines (e.V., 2008) for green roof construction, many manufacturers produce a freely
draining mixture of graded aggregates, with a low total organic content. The composition of commercial
media blends is often considered proprietary information, but even branded products vary according to
materials available locally and at the required time (Rugh, 2013).
Desirable characteristics in a green roof medium are often conflicting, such as the requirement to be
freely draining to prevent ponding or roof membrane damage, impede the rate of percolation (low
permeability) to detain the peak flow discharge, and to retain water. In an effort to balance these demands,
and to improve triple bottom line sustainability indicators, academic research groups have trialed many
materials as components of green roof media. These have included: Rockwool cubes, coconut coir and
Styrofoam pellets, crushed shells, shredded tires, tumbled porcelain, and glass (Steinfeld and Del Porto
2008), crushed brick, clay pellets, paper ash pellets, and carbon8 pellets (Molineux et al. 2009). Despite
interest in the reuse and recycling of some of these more unusual materials, most products on the market
today use a combination of crushed recycle aggregate, lightweight expanded aggregate and composted
organic matter. The diversity of these mixtures, the commercial secrecy about their composition and the
lack of long-term studies leave many unanswered questions for researchers and contractors alike:
“My main problem with most substrates is the missing mid-range particle sizes in quite a few substrates,
and the inconsistency of composts, different compaction rates, and the lack of testing.”(Warmerdam
2013)
Detailed analyses of green roof planting media have been conducted by a few teams seeking to calibrate
their hydrological or thermal models (Hilten et al., 2008; Palla et al., 2012). Ouldboukhitine et al. (2012)
undertook analysis of five branded green roof media, assessing their thermal conductivities at a range of
saturation values. Having determined a ‘preferred’ product, this underwent a more detailed analyses using

15
Dynamic Vapor Sorption (DVS) and mercury porisometry to characterize the pore structure and water
retention properties for a subsequent model.
The percentage organic matter is another significant parameter which has been demonstrated to influence
both vegetation health and water holding capacity (Rowe et al., 2006; Yio et al., 2013). Nagase and
Dunnett (2011) combined their study of plant growth with hydrological characteristics, measuring the
effect of adding up to 50 % additional compost to a commercial crushed brick based product and
concluded that the addition of the compost had a significant, positive impact on the available moisture in
the planting media. They cautioned that application rates ≥ 25% may cause excessively ‘lush growth’ of
the vegetation, which would be unable to withstand later dry periods.
Concerns have been expressed over the shrink-swell characteristic of compost and the potential for
hydrophobicity to develop in green roof media, preventing rewetting in subsequent rainstorms
(Krzeminski, 2013). Doerr et al. (2000) undertook a review of water repellency in natural soils, and
determined that a higher percentage of organic matter, coarser particle sizes and high temperatures were
associated with the development of hydrophobicity; all three of these factors being notable in green roof
media. Multi-modal water retention functions of two related materials, peat and composted pine bark have
been have been described using a “modified van Genuchten-Durner approach” by Naasz et al. (2008).
They reported that the peat samples demonstrated significant hysteresis in the water retention curve (21
%) and concluded that this may be related to the shrink-swell characteristics of the material. The pine
bark, described as “quasi-rigid”, showed much less hysteresis (10 %) and did not show such a great
improvement in the multi-modal (4 pore domains) versus the uni-modal curve fit.
Depth
Mentens et al. (2006) derived a regression equation (R2
= 0.78) relating green roof runoff (RO, mm) to
precipitation (P, mm) and the depth of the planting medium (S, mm), following a literature review
including 125 observed events:
𝑅𝑂 = 693 − 1.15𝑃 + 0.001𝑃2
− 0.8𝑆
Equation 1-4
However, other laboratory controlled and single site studies have failed to concur on a relationship
between depth and retention. Spanning a range often considered to be ultra-light-weight, a study by
VanWoert et al. (2005) found no significant differences in retention over 14 months, when comparing
roofs with 2.5/4/6 cm planting medium depth. Looking at deeper systems, Nardini et al. (2012) reported
no significant difference in the retention of otherwise comparable roofs with 12/20 cm depth of medium,

16
when planted. However, they did find a significant difference between the two control plots at the same
depths, but without vegetation. Kelly (2008) studied paired roofs with 10/20 cm of medium and reported
no significant difference in net water retention. However, a contemporary study in Germany concluded
that medium depth dominated the overall retention of stormwater (Uhl and Schiedt, 2008).
The widely cited FLL guide indicates that the depth of extensive green roofs is not linearly related to net
retention of annual precipitation with increased depth of media failing to retain a proportional amount of
water (see Figure 1-10) (e.V., 2008).
Figure 1-10 Derived from (e.V., 2008), with the caption “All figures relate to locations with annual
precipitation values of 650 – 800 mm where monitoring has been performed over a period of several years.”
The non-linear relationship has been attributed to the high proportion of macropores and subsequent
formation of preferential flow paths in the planting medium (She and Liu, 2013; Vergroesen et al., 2010).
However, observations on preferential flow are not universal, Palla et al. (2009) specifically reported that
the medium in their study demonstrated no significant preferential flows. In a detailed laboratory study of
unplanted green roof media (Yio et al., 2013) concluded that the depth has a significant effect, despite the
inherent heterogeneity of green roof planting medium, as:
“…preferential flow paths are more likely to be interrupted by zones of slower flow in deeper substrates
than shallower ones.”
Another significant factor in the development of the numbers illustrated in Figure 1-10, is the empirical
derivation. All of the monitoring periods will have encompassed storms of varying intensities and total
0
10
20
30
40
50
60
70
0 5 10 15 20
Annualretention(%)
Medium depth (cm)

17
depths, both of which influence the retention properties of extensive green roofs on an event-basis and
thusly, an annual basis.
As depth range spanned by extensive green roofs is relatively narrow (typically ≤ 15 cm) and the
composition of the media varies between studies it has not been possible, to date, to conclude what the
effect of depth alone is on the hydrological performance of green roofs. However, deeper roofs are
associated with improved resiliency of the vegetation, which in turn protects the medium from wind scour
and erosion (Rowe et al., 2011).
Vegetation
The presence or absence of Sedum on the green roof was reported as playing a “minimal” role compared
to that of the planting medium by VanWoert et al. (2005). However, this assertion has since been
challenged; a report by Berghage et al. (2007) concluded that the vegetation can contribute up to 40% of
the hydrological performance of a green roof, depending upon the climatic factors. More recently, a
microcosm study by Bousselot et al. (2011) reported significant differences in the water retention of
medium planted with different species over 18 days. However, the difference between succulents (water-
storing) and herbaceous (broad-leaved) species was obscured to some degree, by variation within these
groups. In Italy, a study of green roofs with varying depth and plant types compared a 200 mm roof
planted with shrubs (multi-stemmed, woody), and a 120 mm roof planted with herbaceous species
(Nardini et al., 2012). They found no significant difference in the retention properties between the two
planting types despite the inclusion of depth of planting medium as a co-variable.
In addition to the contribution that the species have on evapotranspiration rate, by removing water from
the planting medium, the choice of vegetation may also have a significant impact on rainwater
interception. Standing water captured on leaf surfaces is available for direct evaporation, maximizing the
efficiency of removal from the system. The study of intercepted water is largely concerned with forested
ecosystems, but a few interception values exist for herbaceous ground covers. Gerrits (2010), reported
that a grass/moss layer intercepted between 4.1 ± 1.0 mm in the summer, and 2.0 ± 0.9 mm in the winter.
It was noted that the intensity of the individual precipitation events and the inter-event time each had
large effects on these numbers. Gerrits further concluded that whilst many papers detail observations on
vegetation interception, being able to model this process was important to reduce dependency on local
and specific field trials. This sentiment has been echoed by Taylor (2011), who reviewed literature
relating laboratory experiments and ‘early’ models to wetting of leaf surfaces in agricultural applications.
Given the number of complicating factors in the physiology of plants, in terms of their physical form and
metabolic processes, it is unsurprising that a conclusive answer to the role of vegetation has not yet been

18
reached. Even considering plant assemblies as complete ecosystems, conclusive distinctions between the
hydrologic performances of different classes of plants on green roofs remains to be demonstrated.
The Green Roof as an Orifice
Some studies report detailed event data including reduction in peak flow and peak flow detention
(increased lag time) (Carter and Rasmussen, 2006; Kelly, 2008; Liu and Minor, 2005; Moran et al., 2004;
Uhl and Schiedt, 2008; Voyde et al., 2010), Figure 1-11 illustrates these terms qualitatively.
Figure 1-11 Conceptual illustration of effects on hydrograph
Some reduction in peak flow is anticipated during any event, as some portion of the rainfall is retained by
the green roof. Often, shallow rainfall events result in no outflow from the media. Minimum storm depth
which results in outflow could be a way of comparing different installations, were it not for the large
effect of the antecedent conditions on the amount of water retained. This effect, whereby a large number
of events result in 100% peak flow reduction, strongly influences overall results. For example, an average
of 85% reduction in the peak flow was reported by (Moran et al., 2004) in North Carolina, USA. In the
more tropical climate of Auckland, NZ, (Voyde et al., 2010) reported a median peak flow reduction of
93%. Liu and Minor (2005) noted that peak flow reduction was affected by season, ranging between 25 -
60% in the summer and between 10-30% in the fall. Kelly (2008) found that 20 cm of planting medium
reduced the peak flow, compared to 10 cm of the same material (within a 30-minute period). However,
Uhl and Schiedt (2008) did not find the same, reporting instead that depth had no significance in reducing
peak flow. Peak lag increase has been reported by various parties, (Berghage et al., 2009; Carter and
Rasmussen, 2006; Moran et al., 2004), in all studies the delay time varies widely as a function of the
Time
Flowfromroof(Q)
Peak flow reduced and detained
Flow without green roof

19
intensity of the rain event. Green roofs are freely draining and constructed upon a base with a high
transmissivity, as such the peak detention function is determined only by the time taken for percolation
through the media.
An exception to these observations may be where a material of lower transmissivity (such as loose
aggregate) is used in place of the typical drainage board, slowing lateral, subsurface flow (Fassman-Beck
and Miller, 2016; Roofmeadow, 2013). As with all rooftops, the slope, the horizontal flow distance and
downspout design also contribute to the discharge hydrology.
The Green Roof as an Evaporation Pan
In dense urban settings where many forms of LID cannot be practiced, there is only the rooftop with
which to manage the ‘excess’ stormwater. In these cases, engineers, developers and policy makers locally
are often agreed that a subterranean vault or cistern beneath a proposed building is a preferred measure to
achieve annual stormwater retention targets (Cheung, 2016). As a building integrated system, the role of
the green roof is now changed into a means with which to empty this reservoir in between rain events
through irrigation with the harvested rainwater (Figure 1-12).
Figure 1-12 Conceptual closed system model combining a green roof with a cistern
This type of system has been considered by Hardin et al. (2012), who used a mass balance approach to
model a cistern of similar volume to their extensive green roof in Florida, USA. In so doing, the
researchers found that they could double the annual stormwater retention of their system. More recently,
in Shenzhen, China, Qin et al. (2016) used a 1-D HYDRUS model to simulate a green roof with
integrated storage beneath the porous media; the results of their study were framed on the irrigation
requirements of the vegetation rather than the volume of discharge from the system. Similarly, Chao-
Hsien et al., (2015) made conclusions regarding the amount of potable water required to top up their
Irrigation
Green roof
Cistern
EvapotranspirationPrecipitation
Overflow discharge

20
cistern, after also producing a model which integrated a green roof with separate storage in Keelung,
Taiwan.
Retaining and recirculating discharged water provides a number of co-benefits including improved plant
health, increased evaporative cooling and prevents excess nutrient discharge from fertilizer or biological
components within the planting medium. Although this type of system is in use locally (Figure 1-13),
research is required to model the stormwater hydrology of such systems, and the long term effects of
recirculating water could result in increased salinity within the medium (Jones and Jr., 2012; Moritani et
al., 2013).
Figure 1-13 One part of the irrigation system on the Rottman School of Management, University of Toronto.
This system uses recycled and/or harvested rainwater to irrigate extensive green roofs.

21
Thesis Organization
The body of the thesis is prepared as four proposed journal articles and a chapter of smaller, related
studies (Chapters 2 through 6). Chapters 2 though 4 describe measurements and calculations made on the
experimental Green Roof Innovation Testing laboratory (GRITlab) and are complimentary to one another.
Chapter 5 and 6 stand alone as a series of laboratory experiments and a field survey respectively:
 Chapter 2 presents event-based analysis of rainstorms on experimental extensive green roofs at
GRITlab, to compare the influence of four design factors: type of medium, depth of medium,
planting and irrigation. The parameters calculated include monthly and seasonal volumetric
runoff coefficients (Cvol), NRCS curve numbers and peak runoff coefficients (Cpeak).
o A manuscript of this work has been submitted to the ASCE Journal of Hydrologic
Engineering, and is in review, as of August 2016.
 Chapter 3 presents winter water balance data from GRITlab, and aggregates this with the event-
based analysis from the summer in Chapter 2 to determine annual Cvol values under the influence
of the same four design factors. Addressing the issue of irrigation discharge and the potential for
recirculation of excess water is undertaken by considering water balance from three different
irrigation programs.
o A manuscript of this work is currently being prepared for submission as an additional
paper to Chapter 2.
 Chapter 4 considers the physicochemical properties of the GRITlab systems. Stormwater
retention is improved through the use of a compost based planting medium. But this causes
increased phosphorous to be discharged, a problem which could be countered with a recirculation
mechanism. Recirculating water until 100% evapotranspired could result in increased salinity
within the system, so methods of monitoring medium moisture content and dissolved solids in the
medium is presented.
o This chapter includes two separate studies which relate to the preceding chapters, but not
necessarily well to one another. This work was considered for a conference presentation,
but is not currently being prepared for publication.
 Chapter 5 examines the interplay of green roof medium properties and the construction depth to
help explain what limitations simply increasing depth of planting medium has as a design
decision.
o A manuscript of this work has been submitted to the Elsevier Journal of Hydrology, and
is in review, as of August 2016.
 Chapter 6 describes the properties of planting media recovered from thirty-three extensive green
roofs in Southern Ontario.

22
o This study has been accepted for publication in the Elsevier journal, Ecological
Engineering (Hill et al., 2016).
o A supporting photographic journal has been self published and is currently hosted online
(Lottie, 2016).
There are two complimentary studies included in the appendices, which have already been presented as
conference papers: Appendix D reviews a few of the attitudes towards evolving green roof landscapes
from their owners. Appendix E compares methods of erosion control applied on green roofs.
Authorship
The author (Jenny C. Hill) is the primary author of the work and writing presented in this thesis. Dr.
Brent Sleep and Dr. Jennifer Drake have served as co-authors for each of the substantive chapters, as has
Liat Margolis in Chapters 2 and 3.

23
: Influences of Four Extensive Green Roof Design
Variables on Stormwater Hydrology
Abstract
This study assesses the relative influence of four independent variables on green roof hydrological
performance under rainstorm conditions. Twenty-four extensive green roofs representing all combinations
of four design factors were used: native meadow species versus Sedum, mineral-based versus
biologically-derived planting medium, 10 cm versus 15 cm depth, and irrigation provided daily, sensor
controlled, or not at all. From events covering the summer period May – October in 2013 and 2014, mean
values were determined for the seasonal volumetric runoff coefficient (Cvol = 0.4), peak runoff coefficient
(Cpeak = 0.12), and NRCS curve number, (CN = 90). Irrigation had the largest overall impact; daily
irrigation increased Cvol to 0.5 compared to 0.3 for systems with sensor controlled or no irrigation. The
biologically-derived planting medium, comprised of a high proportion of aged wood compost, made a
significant improvement, maintaining Cvol of 0.3 compared to 0.4 for the mineral-based product in the
modules without irrigation. A similar pattern was found in the NRCS curve numbers.

24
Introduction
Research on the hydrology of green roofs has established that a combination of lightweight planting
media and environmentally resilient vegetation on building roofs can improve the rainwater runoff
characteristics from buildings compared to ‘traditional’ non-permeable alternatives (Czemiel Berndtsson,
2010; Lundholm et al., 2010; Nagase and Dunnett, 2011; Van Seters et al., 2009). Widespread
deployment across an urbanized watershed of green roofs provides a valuable contribution to stormwater
control by making optimal use of the limited available catchment surfaces (Carter and Jackson, 2007). In
many cities, this ambition will require many retrofit installations and of the systems available, “extensive”
green roofs are thinnest (up to 15 cm), usually the most lightweight, cheapest and most likely to be
deployed (Oberndorfer et al., 2007). Therefore, it is extensive green roofs that are most likely to have the
highest impact potential on urban watersheds.
They typically are constructed from a number of layers, as shown in Figure 2-1. Each of these layer
components require design decisions, which are often driven by market forces owing to a plethora of
available proprietary products and solutions. From the rooftop up, the first hydraulically significant
component is a drainage/ retention layer, which reduces or eliminates pooling of water on the waterproof
roof membrane and ensures that the root zone is not saturated for extended periods.
Figure 2-1 Typical layering of a built-up extensive green roof system
A very common format for the drainage/retention layer is a pre-formed rigid polymer sheet or board, with
regularly placed drainage holes, such that depressions in the sheet form small reservoirs of water held
away from the underlying roof. A geotextile is usually employed on top of the drainage board to keep the
drainage board depressions free from excess particulate matter, because the next substantial component is
a lightweight, engineered, (usually soilless) porous planting medium. A large number of materials have
been trialed and blended for planting medium, with varying degrees of success in terms of stormwater
control and/or vegetation survival (Farrell et al., 2012; Molineux et al., 2009; Ouldboukhitine et al., 2012;

25
Steinfeld and Del Porto, 2008). Influences on the development of green roof planting media have come
from the horticulture and nursery industries, where potting mixtures have been studied for many years,
but even more so from the German FLL agency, who advocate for a much lower proportion of organic
material (< 6.5 %) than would typically be used in nursery potting mixtures (e.V., 2008). Two distinct
schools of thought about the role of organic matter in green roof media exist; those in favor of following
the FLL guidelines, state that biologically derived materials will biodegrade and lose porosity, have
reduced drainage and remain waterlogged, to the detriment of the planting. Those who prefer to specify
high organic matter planting media, containing a higher proportion of compost or other biologically
derived materials believe that these claims are unsupported and unjustified (Buist and Friedrich, 2008).
The choice of vegetation has also received much attention, as it is the most immediately visible,
contributing co-benefits such as aesthetic appeal and habitat for biodiversity and urban ecosystem
support. A significant body of green roof work has focused on the survival of plants, with fewer studies
assessing the hydrological impacts. Some research finds that plants play an important role in hydrology
(Berghage et al., 2007; Bousselot et al., 2011; Lundholm et al., 2010), whilst others have not discerned
any significant impact (Nardini et al., 2012; VanWoert et al., 2005). To support growth and survival of
vegetation, the use of supplementary irrigation is a widespread practice. Secondary benefits can include
aiding in fire prevention by keeping plants green rather desiccated in the height of summer, and reducing
loss of the granular planting medium from wind erosion or scour.
To support the growing application of extensive green roofs as effective tools in stormwater management
strategies, it is important to have accurate values for commonly used hydrologic parameters. In our study,
aggregated volumetric runoff coefficients (Cvol), have been determined, in keeping with other similar
studies (Fassman-Beck et al., 2015; Gregoire and Clausen, 2011). For individual event calculations US
Natural Resources Conservation Service (NRCS) Curve Numbers have been calculated. To provide the
most accurate parameter for peak flow calculations using the Rational method, peak flow runoff
coefficients (Cpeak) were derived using paired peak flow and peak storm intensity data (Young et al.,
2009). Many previous studies have reported observations and derivations of these hydrological
characteristics for green roofs (Czemiel Berndtsson, 2010); these parameters are dependent on the
climatic conditions under which they are measured (Fassman-Beck et al., 2015).
This study is designed to provide useful engineering information for extensive green roofs pertinent to a
humid continental climate (Dfa/Dfb) region (Kottek et al., 2006). The event-based analyses are
constrained to the summer period, encompassing May through to October; these are the months in which
all precipitation was received as rain. Within this context, the objectives are: to determine appropriate
values for the coefficients and parameters given above, to assess the robustness of such parameters with

26
respect to changes in green roof design with respect to: vegetation selection, planting medium type and
depth, and irrigation, and, to identify the preferred option for each of the most influential design factors.
Methods
Green Roof Innovation Testing laboratory
The experimental site, the Green Roof Innovation Testing laboratory (GRITlab) is located on the fifth
storey roof of the historic John H. Daniels Building, situated in the centre of the downtown St. George
Campus of the University of Toronto, Ontario. The lab has twenty-four individual green roof modules,
each with a 2.86 m2
drainage area (2.36 m x 1.21 m), constructed with 2% slope. The modules are
suspended 0.8 m above the roof deck to accommodate instruments and maintenance requirements
(Margolis, 2013).This study assesses four design variables, using a spatially randomized full factorial
(23
3) design; vegetation type, planting media type and planting media depth were considered at two
levels, whilst irrigation provision was tested at three levels. The grid layout of the modules and the
randomized distribution of the variables is presented in Figure 2-2.
Figure 2-2 Schematic of GRITlab, illustrating the randomized layout of the four experimental variables. Key -
colours in each rectangular module can be read from west to east. Vegetation: dark = Meadow, light =
Sedum. Planting medium: dark = biological, light = mineral. Irrigation: dark = daily, light = sensor, mid =
none. Construction depth: dark = 15 cm, light = 10 cm.
Two types of vegetation were considered, a Sedum. blend initially containing 23 cultivars pre-
established onto mats, and a meadow mix of 19 species including grasses and forbs. Both the
meadow seeding and the Sedum. mats were installed in 2011. Further details regarding the plant
communities and their growth performance in previous years has been published (MacIvor et al., 2013).

27
The two types of planting media were selected as representative of the extremes in commercial use
locally: the mineral based medium comprises a large proportion of lightweight expanded aggregates and
crushed brick, and has low organic matter content in concordance with FLL recommendations (e.V.,
2008). The second type is a biologically based medium containing a matured, screened, pine bark
compost with < 5% additional components. The manufacturer’s specification for each product is
presented in Table 2-1. Each of these two materials were tested at 10 and 15 cm depth.
Table 2-1 Physical data for grit planting media, according to manufacturer’s ASTM 2399 report (Bioroof
Systems, 2011)
Mineral Biological
Dry density g/cm3 >0.8 0.58
Saturated density g/cm3 1.28 1.1
Maximum water holding capacity 45% >60%
Saturated hydraulic conductivity cm/s >0.02 >0.01
Organic Matter (%) < 9% >70%
Irrigation was provided to the modules via drip lines, with 300 mm spacing of the emitters. The flow rate
was fixed, and the irrigation controlled by altering the timer program. The daily modules received
irrigation every morning, which maintained a high level of saturation in the media throughout the months
of application. The sensor controlled modules each had a custom adapted, fluid-filled tensiometer
installed (Irrometer) which was set to open the irrigation valve for media moisture tension < -25 kPa;
irrigation was only received by sensor modules, if the valve was open due to dryness of the media. Both
forms of irrigation produced measurable runoff. In 2013 the irrigation system was deployed between the
first week of May and the last week of October; and in 2014 this was reduced to include just the months
of June and September. Further details about the irrigation programming are given in Chapter 3.
Precipitation was measured on site using a tipping bucket rain gauge (TE525M Texas Electronics), whilst
parameters used for the automated calculation of reference evapotranspiration were measured using an
adjacent weather station (Allen et al., 2005): Wind monitor (RM Young), CMP 11 pyranometer (Kipp
and Zonen), HMP45C relative humidity and temperature probe (Campbell Scientific). On twelve
occasions between August 2014 and August 2015 manual rain gauges were placed adjacent to all green
roof modules and single event stormwater collected. These were used for spatial assessment of the rainfall
distribution across the laboratory roof. Planting media moisture content was recorded (5TE, Decagon
Devices) after recalibration for the dielectric properties of each of the two planting media types.
Discharged water from each module was measured using a rain gauge (TB6, Hydrological Services).
These rain gauges were adapted to handle the higher flows experienced, using customized 3D printed
funnels (J Hill et al., 2015). The data logger controlling all of the sensors recorded five-minute resolution
and the data presented here were collected during the months of May to October in 2013 and 2014.

28
Theory and Calculations
As extensive green roofs are relatively small catchments and have very short flow paths, they are highly
responsive to rainfall characteristics with discharge beginning and ceasing rapidly. In most cases at
GRITlab measurable drainage had ceased in less than an hour after a storm had passed, and peak lag
times were not discernable within the 5-minute resolution of the data logger. For this reason, an inter-
event time of one hour was used to determine separate rainfall events in the summer months. So, a storm
was considered to be any rainfall event of ≥ 0.2 mm of rainfall preceded and followed by a minimum of 1
hr without measurable precipitation. Spatial autocorrelation of rainfall patterns across the GRITlab was
assessed using Local Moran’s I values, generated using GeoDa (Anselin et al., 2006). Rainfall
distributions for each summer period were fitted to a single parameter exponential function using Easyfit
(Drokin, 2010):
𝑓(𝑝) = 𝜁𝑒(−𝜁𝑝)
Equation 2-1
Irrigation supply and discharge were not included in water balance calculations. Instead irrigation
provision was included as a categorical variable; none, sensor controlled, or daily. So, the aggregated
(monthly and seasonal) volumetric runoff coefficients (Cvol) have been calculated as the sum the total
discharge depth of the individual event discharge (Q, mm), as a proportion of the sum of the event total
precipitation depth (P, mm):
𝑪 𝒗𝒐𝒍 =
∑ 𝑸
∑ 𝑷
Equation 2-2
Comparisons between group means of the independent variables were made using regression trees
(Demšar et al., 2013). At each level on the trees, the group mean value and number of contributing
modules are presented. The technique then identifies the single factor which provides the greatest
difference in group means and classifies data accordingly. This continues through successive branches
until no significant difference in the group means can be elucidated. The trees were pruned when no
practical difference was discerned in the parameter being fitted.
To assess the event-based stormwater retention and theoretical storage capacity of the modules, NRCS
curve numbers were generated for all twenty-four modules, for the summer months of 2013 and 2014.
The benefit of using curve numbers in this research is that this allows aggregation of the
precipitation/discharge volume of many storm events and reduces one dimension of the data to permit
statistical comparisons between the multivariate designs. Calculations were performed on natural data. i.e.

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